
import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel

def a__       (			A_=32,	A_=10,	A_=100,	A_=1026,	A_=True,	A_="data/tokenized_stories_train_wikitext103.jbl",	A_="igf_context_pairs.jbl",	):






  '''simple docstring'''





  set_seed(3   )
  # generate train_data and objective_set
  __magic_name__	,       __magic_name__								=			generate_datasets(
      A_,	A_,	number=A_,	min_len=1026,	trim=A_   )
  # keeps model same across runs
  set_seed(4   )
  # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
  # can we train on GPU?
  __magic_name__								=			torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )

  # load pretrained model
  __magic_name__								=			load_gpta("""gpt2"""   ).to(A_   )
  print("""computing perplexity on objective set"""   )
  __magic_name__								=			compute_perplexity(A_,	A_,	A_   ).item()
  print("""perplexity on objective set:""",	A_   )

  # collect igf pairs and save to file demo.jbl
  collect_objective_set(A_,	A_,	A_,	A_,	A_,	A_,	A_,	A_   )

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()

def a__       (			A_,	A_=15,	A_=128,	A_=100,	A_="igf_model.pt",	):






  '''simple docstring'''





  set_seed(42   )

  # Load pre-trained model
  __magic_name__								=			GPTaLMHeadModel.from_pretrained("""gpt2"""   )

  # Initialize secondary learner to use embedding weights of model
  __magic_name__								=			SecondaryLearner(A_   )

  # Train secondary learner
  __magic_name__								=			train_secondary_learner(
      A_,	A_,	max_epochs=A_,	batch_size=A_,	eval_freq=100,	igf_model_path=A_,	)

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner

def a__       (			A_,	A_,	A_,	A_=32,	A_=1000,	A_=16,	A_=1.0,	A_=recopy_gpta,	A_=None,	A_=10,	A_="gpt2_finetuned.pt",	):






  '''simple docstring'''





  __magic_name__								=			torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
  __magic_name__								=			RandomSampler(A_   )
  __magic_name__								=			DataLoader(A_,	sampler=A_   )

  __magic_name__								=			max_steps // (len(A_   )) + 1
  __magic_name__								=			0
  __magic_name__								=			torch.zeros((1, context_len),	dtype=torch.long,	device=A_   )
  __magic_name__	,       __magic_name__	,       __magic_name__								=			recopy_model(A_,	A_,	A_   )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(A_   )
    secondary_learner.eval()
  __magic_name__								=			[]
  __magic_name__								=			0

  __magic_name__								=			[]
  __magic_name__								=			[]

  # Compute the performance of the transformer model at the beginning
  __magic_name__								=			compute_perplexity(A_,	A_,	A_   )
  test_perps.append(A_   )
  print("""Test perplexity, step""",	A_,	""":""",	A_   )
  for epoch in range(int(A_   )   ):
    for step, example in enumerate(A_   ):
      torch.cuda.empty_cache()
      __magic_name__								=			random.randint(0,	example.size(2   ) - context_len - 1   )
      __magic_name__								=			example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      __magic_name__								=			model(A_,	labels=A_   )
      __magic_name__								=			True

      if secondary_learner is not None:
        __magic_name__								=			secondary_learner.forward(
            torch.tensor(A_,	dtype=torch.long,	device=A_   ).unsqueeze(0   )   )[0].item()
        observed_qs.append(float(A_   )   )

        # Here we implement the simple non-constant threshold for the predicted IG(X) value
        # We will decay the selectivity of our secondary learner filter from
        # 1 standard deviation above average to 1 below average after 10 batches.

        if global_step == 10:
          __magic_name__								=			-1
        if predicted_q < threshold:
          __magic_name__								=			False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()   )   )
        __magic_name__								=			outputs[0]
        lm_loss.backward()
        examples += 1

      del outputs

      # Once the batch is filled with enough contexts, backprop on the batch.
      if examples == batch_size:
        torch.cuda.empty_cache()
        __magic_name__								=			0
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters(),	3.0   )
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule
        global_step += 1
        # Compute the performance of the transformer model at this batch
        if global_step % eval_interval == 0:
          __magic_name__								=			compute_perplexity(A_,	A_,	A_   )
          test_perps.append(A_   )

          print("""Test perplexity, step""",	A_,	""":""",	A_   )
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 60:
        break
    if max_steps > 0 and global_step > 60:
      break

    # save finetuned transformer model
  torch.save(model.state_dict(),	A_   )
  torch.cuda.empty_cache()
  # Do some cleaning up so we can reinitialize for the next run of this function
  del lm_optimizer
  del lm_scheduler
  return model

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""   )

  # Required parameters
  parser.add_argument(
      """--data_dir""",	default=A_,	type=A_,	required=A_,	help="""The input data dir. Should contain data files for WikiText.""",	)
  parser.add_argument(
      """--model_name_or_path""",	default=A_,	type=A_,	required=A_,	help="""Path to pretrained model or model identifier from huggingface.co/models""",	)
  parser.add_argument(
      """--data_file""",	type=A_,	default=A_,	help=(
          """A jbl file containing tokenized data which can be split as objective dataset, """
          """train_dataset and test_dataset."""
      ),	)

  parser.add_argument(
      """--igf_data_file""",	type=A_,	default=A_,	help="""A jbl file containing the context and information gain pairs to train secondary learner.""",	)

  parser.add_argument(
      """--output_dir""",	default=A_,	type=A_,	required=A_,	help="""The output directory where the final fine-tuned model is stored.""",	)

  parser.add_argument(
      """--tokenizer_name""",	default=A_,	type=A_,	help="""Pretrained tokenizer name or path if not the same as model_name""",	)
  parser.add_argument("""--seed""",	type=A_,	default=A_,	help="""A seed for reproducible training."""   )

  parser.add_argument(
      """--context_len""",	default=32,	type=A_,	help=(
          """The maximum total input sequence length after tokenization. Sequences longer """
          """than this will be truncated, sequences shorter will be padded."""
      ),	)

  parser.add_argument(
      """--size_objective_set""",	default=100,	type=A_,	help="""number of articles that are long enough to be used as our objective set""",	)
  parser.add_argument(
      """--eval_freq""",	default=100,	type=A_,	help="""secondary model evaluation is triggered at eval_freq"""   )

  parser.add_argument("""--max_steps""",	default=1000,	type=A_,	help="""To calculate training epochs"""   )

  parser.add_argument(
      """--secondary_learner_batch_size""",	default=128,	type=A_,	help="""batch size of training data for secondary learner""",	)

  parser.add_argument(
      """--batch_size""",	default=16,	type=A_,	help="""batch size of training data of language model(gpt2) """   )

  parser.add_argument(
      """--eval_interval""",	default=10,	type=A_,	help=(
          """decay the selectivity of our secondary learner filter from"""
          """1 standard deviation above average to 1 below average after 10 batches"""
      ),	)

  parser.add_argument(
      """--number""",	default=100,	type=A_,	help="""The number of examples split to be used as objective_set/test_data"""   )

  parser.add_argument(
      """--min_len""",	default=1026,	type=A_,	help="""The minimum length of the article to be used as objective set"""   )

  parser.add_argument(
      """--secondary_learner_max_epochs""",	default=15,	type=A_,	help="""number of epochs to train secondary learner"""   )

  parser.add_argument("""--trim""",	default=A_,	type=A_,	help="""truncate the example if it exceeds context length"""   )

  parser.add_argument(
      """--threshold""",	default=1.0,	type=A_,	help=(
          """The threshold value used by secondary learner to filter the train_data and allow only"""
          """ informative data as input to the model"""
      ),	)

  parser.add_argument("""--finetuned_model_name""",	default="""gpt2_finetuned.pt""",	type=A_,	help="""finetuned_model_name"""   )

  parser.add_argument(
      """--recopy_model""",	default=A_,	type=A_,	help="""Reset the model to the original pretrained GPT-2 weights after each iteration""",	)

  # function calls
  # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
  generate_n_pairs(
      context_len=32,	max_steps=10,	size_objective_set=100,	min_len=1026,	trim=A_,	data_file="""data/tokenized_stories_train_wikitext103.jbl""",	igf_data_file="""igf_context_pairs.jbl""",	)

  # Load train data for secondary learner
  __magic_name__								=			joblib.load("""data/IGF_values.jbl"""   )

  # Train secondary learner
  __magic_name__								=			training_secondary_learner(
      A_,	secondary_learner_max_epochs=15,	secondary_learner_batch_size=128,	eval_freq=100,	igf_model_path="""igf_model.pt""",	)

  # load pretrained gpt2 model
  __magic_name__								=			GPTaLMHeadModel.from_pretrained("""gpt2"""   )
  set_seed(42   )

  # Generate train and test data to train and evaluate gpt2 model
  __magic_name__	,       __magic_name__								=			generate_datasets(
      context_len=32,	file="""data/tokenized_stories_train_wikitext103.jbl""",	number=100,	min_len=1026,	trim=A_   )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      A_,	A_,	A_,	context_len=32,	max_steps=1000,	batch_size=16,	threshold=1.0,	recopy_model=A_,	secondary_learner=A_,	eval_interval=10,	finetuned_model_name="""gpt2_finetuned.pt""",	)


if __name__ == "__main__":
    main()



from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			prime_factors(A_   )
  if is_square_free(A_   ):
    return -1 if len(A_   ) % 2 else 1
  return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()

